Color and texture match ratings for optimal match selection

ABSTRACT

A computer system for analyzing a paint sample and generating values that describe various attributes of a proposed matching color can comprise instructions for receiving from a coating-measurement instrument one or more coating variables of a target coating. The system can also comprise instructions for calculating coating texture ratings for the multiple respective proposed coating matches. The coating texture ratings can indicate a similarity between the one or more coating texture characteristics of the target coating and respective coating texture characteristics of each of the respective proposed coating matches. Additionally, the system can comprise instructions for sending instructions to generate a user interface that depicts overall rankings of at least a portion of the proposed coating matches. The overall rankings indicate a similarity between the target coating and each of the at least a portion of the proposed coating matches with respect to the coating texture ratings.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/047,950 filed on Feb. 19, 2016 and entitled “COLOR AND TEXTURE MATCHRATINGS FOR OPTIMAL MATCH SELECTION,” which application is expresslyincorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Coatings have been used for hundreds of years for protection and to addvisual appeal to products and structures. For example, houses arepainted or stained in order to protect the underlying siding from theweather and also to add aesthetic qualities to a house. Similarly,automobiles are painted, sometimes with multiple purpose-made layers, toprotect the metal body of the vehicle and also to add visual appeal tothe vehicle.

Various coatings may have specific features and properties that arebeneficial or desirable for certain uses. For example, differentcoatings can have different electrical conductive properties, differentchemical reactivity properties, different hardness properties, differentUV properties, and other different use-specific properties.Additionally, coatings may comprise unique visual features. For example,some automotive coatings comprise texture features that give the coatingunique visual effects.

The ability to provide highly consistent coating compositions is animportant aspect in many different coating markets. For example, it isdesirable for decorative coatings to comprise consistent colors andvisual features. Similarly, the ability to match previously appliedcoatings to available coating colors is important. For example, whenfixing a scratch in a car's coating, it is desirable to match both thecolor and the texture of the original coating. The ability to matchcoatings requires both consistent coating compositions and tools forcorrectly identifying the target coating and/or identifying anacceptable composition to match the target coating.

Significant technical difficulties exist in providing complex coatingand texture information to end users. For example, coating informationinvolves large numbers of distinct measurements from different angles.The resulting datasets can be large and difficult to use in practice. Assuch, there is a need for technically sound methods and schemes forprocessing large coating datasets and presenting the resultinginformation to end users in consistent terms that are easy to use andunderstand.

BRIEF SUMMARY OF THE INVENTION

Implementations of a computer system for analyzing a paint sample andgenerating values that describe various attributes of a proposedmatching color can comprise instructions for receiving from acoating-measurement instrument one or more coating sparklecharacteristics of a target coating. The system can also compriseinstructions for calculating sparkle ratings for the multiple respectiveproposed coating matches. The sparkle ratings can indicate a similaritybetween the one or more coating sparkle characteristics of the targetcoating and respective coating sparkle characteristics of each of therespective proposed coating matches. Additionally, the system cancomprise instructions for sending instructions to generate a userinterface that depicts overall rankings of at least a portion of theproposed coating matches. The overall rankings indicate a similaritybetween the target coating and each of the at least a portion of theproposed coating matches with respect to the sparkle ratings.

Additionally, implementations for a computerized method for matching apaint sample to various proposed paint coatings can comprise an act ofreceiving one or more coating characteristics of a target coating from acoating-measurement instrument. The method can also comprise an act ofdisplaying effect texture ratings for multiple respective proposedcoating matches on a digital display device. The effect texture ratingscan indicate a similarity between the one or more coatingcharacteristics of the target coating and respective coatingcharacteristics of each of the respective proposed coating matches.Additionally, the method can include an act for ordering at least aportion of the proposed coating matches. The ordering indicates astrength in similarity between the target coating and each of the atleast a portion of the proposed coating matches with respect to thecoating texture ratings.

Further, implementations of a computer program product can compriseinstructions for a method that include an act of receiving from acoating-measurement instrument one or more coating variables and one ormore coating sparkle variables of a target coating. Additionally, themethod can comprise an act of calculating effect texture ratings formultiple respective proposed coating matches. The effect texture ratingscan indicate a similarity between the one or more effect texturecharacteristics of the target coating and respective effect texturescharacteristics of each of the respective proposed coating matches.Further, the method can comprise an act of calculating sparkle ratingsfor the multiple respective proposed coating matches. The sparkleratings can indicate a similarity between the one or more coatingsparkle characteristics of the target coating and respective coatingsparkle characteristics of each of the respective proposed coatingmatches. Further still, the method can comprise an act of generating auser interface that depicts overall rankings of at least a portion ofthe proposed coating matches. The overall rankings can indicate asimilarity between the target coating and each of the at least a portionof the proposed coating matches with respect to the coating textureratings and the texture color ratings.

Additional features and advantages of exemplary implementations of theinvention will be set forth in the description which follows, and inpart will be obvious from the description, or may be learned by thepractice of such exemplary implementations. The features and advantagesof such implementations may be realized and obtained by means of theinstruments and combinations particularly pointed out in the appendedclaims. These and other features will become more fully apparent fromthe following description and appended claims, or may be learned by thepractice of such exemplary implementations as set forth hereinafter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above recited and otheradvantages and features of the invention can be obtained, a moreparticular description of the invention briefly described above will berendered in the following detailed description by reference to theappended drawings. Understanding that these drawings depict onlyexemplary or typical implementations of the invention and are nottherefore to be considered to be limiting of its scope, the inventionwill be described and explained with additional specificity and detailthrough the use of the accompanying drawings in which:

FIG. 1 depicts a schematic diagram of a system for calculating a coatingtexture in accordance with implementations of the present invention;

FIG. 2A depicts a relative texture characteristic chart and accompanyingexample coatings in accordance with implementations of the presentinvention;

FIG. 2B depicts another relative texture characteristic chart andaccompanying example coatings in accordance with implementations of thepresent invention;

FIG. 2C depicts yet another relative texture characteristic chart andaccompanying example coatings in accordance with implementations of thepresent invention;

FIG. 3 depicts a line chart of relative texture characteristics inaccordance with implementations of the present invention;

FIG. 4 depicts a table of texture variables in accordance withimplementations of the present invention;

FIG. 5 depicts a graph of a correlation of perceived texturecharacteristics in accordance with implementations of the presentinvention;

FIG. 6 depicts an exemplary coating texture information user interfacein accordance with implementations of the present invention;

FIG. 7 depicts another exemplary coating texture information userinterface 700 in accordance with implementations of the presentinvention; and

FIG. 8 depicts a flow chart of steps within a method for matching apaint sample to various proposed paint coatings in accordance withimplementations of the present invention.

DETAILED DESCRIPTION

The present invention generally relates to a method, interpretationprocess, and apparatus for providing assessments of colorimetric andphysical property attributes of cured simple and complex paint mixturesinside and outside of a laboratory environment. It further explains aunique methodology to utilize the supplied information to select anoptimal match for appropriate matches in a database or other appropriatesample, or quality assurance purposes.

The present invention extends to systems, methods, and apparatusconfigured to characterize a target coating with respect to one or morepreviously analyzed reference coatings. Herein, computer systems anddata acquisition devices may be used to gather texture information froma target coating and generate one or more texture outputs that describethe target coating relative to one or more other coatings. The presentinvention may employ computer systems and data acquisition devices forreceiving large data sets of texture variables and transforming thelarge dataset into simplified and readily useable texture valueindicators. Further, the present invention may also comprise datatransformations for mapping unprocessed texture variables tohuman-perceived texture characteristics. Implementations of the presentinvention provide novel and non-obvious improvements to the field ofcoating matching.

Accordingly, the present invention provides novel and innovative systemsand methods for analyzing and matching coating textures. In contrast toconventional methods of displaying texture differences, the presentinvention can provide simple and clear information that isunderstandable by a lay person. Additionally, the present invention canprovide a true visual texture match for an analyzed coating. Inparticular, according to the present invention a target coating may bematched to reference data that is based upon visual impressions of alarge cross-section of the general population. As such, the presentinvention can provide a simpler and more accurate means for analyzingand matching coating texture.

At least one implementation of the present invention can comprise acoating texture calculation software application 100. FIG. 1 depicts aschematic diagram of a system for calculating a coating texture. Inparticular, FIG. 1 shows the software application 100 can comprise adata input module 120 that is configured to receive target coatingvariables from an image and coating color variables from a targetcoating. As used herein, the data input module 120 comprises anapplication program interface (“API”) for communicating with the coatingtexture calculating software application 100, a user interface forcommunicating with the coating texture calculating software application100, or any other function configured to receive input into and/or sendoutput out of the coating texture calculating software application 100.The target coating variables may comprise target coating variablesgenerated from an image of a target coating.

For example, FIG. 1 shows that the data input module 120 may be incommunication with a coating-measurement instrument, such as acamera-enabled spectrophotometer 110 that provides the softwareapplication 100 with a set of target coating variables for a targetcoating. As used herein, target coating variables comprise texturerelated data received from either a spectrophotometer or from analgorithm that has processed an image of a coating. The target coatingvariables may comprise a variety of different measurements relating tothe target coating, including readings at a wide-range of differentangles. Additionally, in at least one implementation, the actual datareceived from the camera-enabled spectrophotometer may be dependent onthe type and brand of camera-enabled spectrophotometer. For instance,different brands of camera-enabled spectrophotometers may measuredifferent characteristics of a coating and may perform unique internalprocessing on the data before communicating it to a computer for furtherprocessing. As such, coating variables can comprise a wide variety ofdifferent forms and values depending upon how the specific data wasinitially gathered and processed.

In alternate implementations, the data input module 120 may directlyreceive an image of a coating. The received image may comprise aphotograph taken with at least three-times optical zoom with a digitalcamera. The data input module 120 may be configured to analyze the imageof the coating and calculate desired texture variables. In at least oneimplementation, a black-and-white image is utilized to calculate the setof texture variables for the target coating because calculations can besimplified by removing color information. In contrast, in at least oneimplementation, a color image can be used to calculate the set oftexture variables for the target coating because additional textureinformation may be available in a color image that would not otherwisebe accessible in a black-and-white image.

FIG. 1 further shows that data input module 120 can provide the coatingcolor variables to a color match module 170. The color coating variablescan be received from a spectrometer and processed using conventionalmethods. Using the coating color variables, the color match module 170can search a coating information database 140 for one or more colorsthat most closely match the color of the target coating. In at least oneimplementation, each of the colors stored within the coating informationdatabase 140 can be associated with a particular coating and withcoating variables. For example, the color match module 170 may determinethat the target coating comprises a forest green color that is similarto a particular group of colors stored within the coating informationdatabase 140.

Once one or more proposed matching colors have been identified, thecolor match module 170 can provide the texture calculating module 130with indicators of the proposed matches. The indicators can comprisepointers to the proposed matches within the coating informationdatabase, data structures comprising information about each proposedmatch, or any other data communication that provides the texturecalculating module 130 with access to the necessary coating informationfor the proposed matches. As shown in FIG. 1, the texture calculationmodule 130 can then access, from within the coating information database140, the coating variables that are associated with each of the one ormore proposed matching coatings.

Using the coating variables associated with the proposed matchingcoatings and the coating variables associated with the target coating,the texture calculation module 130 can calculate a correlation betweenthe target coating and each of the proposed matching coatings. Basedupon the calculated correlation, the texture calculation module 130 cancalculate a set of relative texture characteristics for the proposedmatching coating that indicate relative differences in texture betweenthe proposed matching coating and the target coating. Each of therelative texture characteristics can comprise an assessment over allangles of the target coating.

The relative texture characteristics may be based on human-providedrelative visual impressions between different reference coatings. Forexample, the relative visual impressions can comprise a relativecoarseness, a relative sparkle intensity, and/or a relative sparkledensity with respect to a plurality of different reference coatings. Therelative impressions can be gathered by having a large group of diverseindividuals view several different coating samples with respect to eachother. The individuals can then state their impression as to varioustexture characteristics of the samples.

For instance, the individuals may be asked to rate the respectivesamples as having relatively more or less overall texture on a numericscale. Similarly, the individuals can be asked to rate the respectivesamples on a relative scale with respect to coarseness, sparkleintensity, and/or sparkle density. The relative impressions can then bestatistically mapped to coating variables that are associated with eachof the respective samples. Accordingly, a statistical correlation can becreated between each of the coating variables received from thespectrophotometer and the human perception of various texturecharacteristics.

The texture calculation module 130 can utilize the statisticalcorrelation to identify a set of relative texture characteristics of thetarget coating with respect to each of the proposed coating matches. Forexample, the texture calculation module 130 can calculate a relativecoarseness value, a relative texture density value, and/or a relativetexture intensity value. Additionally, the texture calculation module130 can calculate an overall relative texture characteristic value basedupon the set of relative texture characteristics. For example, theoverall relative texture characteristic value can be directly derivedfrom correlation to human perception, or the overall relative texturecharacteristic value can be calculated from an average of other relativetexture data.

The display module 150 can then display the identified relative texturecharacteristics to a user (e.g., at display 160) on a graphical userinterface, such that the user can easily identify the difference intexture characteristics between the target coating and each of theproposed matching coatings. The displayed relative texturecharacteristics may comprise the single overall texture value, therelative coarseness value, the relative texture density value, and/orthe relative texture intensity value. As such, various implementationsof the present invention can significantly simplify and standardize thetexture information that is displayed to an end user.

Providing a simple indication of a human-perceived difference betweenone or more coatings can provide significant improvements to thetechnical field of coating matching. In particular, providing aconsistent and standard basis for distinguishing texture attributes of acoating addresses significant shortcomings in the technical art. Assuch, utilizing a statistically standardized approach to utilizinghuman-perception of texture differences can provide an innovative methodfor matching coating textures. For example, in at least oneimplementation, relative texture values can be provided with respect toall available coating compositions, such that it is not necessary toidentify specific potential matching coatings in order to generaterelative texture values. Instead, standardized texture values can becalculated based upon a large color space.

FIG. 2A depicts an example of a chart 230 derived from gathering andutilizing human-perceived texture differences within implementations ofthe present invention. In particular, FIG. 2A depicts a first examplecoating 200, a second example coating 210 and a human-perspectivetexture comparison chart 230. While the first example coating 200 andthe second example coating 210 are depicted in image form in FIG. 2A,when presented to a user, the first example coating 200 and the secondexample coating 210 can also be actual painted and cured panels. Assuch, the user(s) are provided with true and accurate representations ofthe final coating color and texture.

The human-perspective texture comparison chart 230 is directed towardsdifferences in visual appearance between the first example coating 200and the second example coating 210. For example, the human-perspectivetexture comparison chart 230 requests that a human user indicate whetherthey perceive that the first example coating 200 comprises more or lessoverall perceived texture than the second example coating 210. Asindicated by the human-perspective texture comparison chart 230 of FIG.2A, a human user may be asked to rate the two example coatings 200, 210with regards to a variety of different texture characteristics. Eachrating may be provided using a pre-defined scale of rating options260(a-e).

A large number of users with different racial, gender, and otherdemographic differences can be asked to compare the same two examplecoatings 200, 210 and provide their own respective perceived texturedifferences. The total resulting perceptions of the variety of users canthen be respectively summarized such that a typical, or most-likely,predicted human-perceived texture comparison for each requested texturequestion is calculated.

In the example depicted in FIG. 2A, the user determined that the firstexample coating 200 comprises a “little less” overall perceived texturethan the second example coating 210. Additionally, FIG. 2A shows thatthe user determined that the first example coating 200 comprises“relatively equal” perceived coarseness to the second example coating210. Further, FIG. 2A shows that the user determined that the firstexample coating 200 comprises “a lot less” sparkle intensity than thesecond example coating 210. Further still, FIG. 2A shows that the userdetermined that the first example coating 200 comprises a “little less”sparkle density than the second example coating 210.

FIG. 2B depicts a similar human-perspective texture comparison chart 230to the one depicted in FIG. 2A. In FIG. 2B, however, the chart showsthat the user compares the first example coating 200 to a third examplecoating 220. As indicated by the human-perspective texture comparisonchart 240 of FIG. 2B, the third example coating 220 comprises adifferent texture characteristic profile than either the first examplecoating 200 or the second example coating 210.

FIG. 2C depicts yet another human-perspective texture comparison chart230. In this particular depiction, a user is comparing the third examplecoating 220 to the second example coating 210. Accordingly, FIGS. 2A-2Cillustrate several different results that may be derived fromhuman-perceived comparisons between a variety of different examplecoatings and to provide human-perspective comparisons between theexample coatings across a range of different texture characteristics.The human-perspective comparisons provided to the human-perspectivetexture comparison charts 230 of FIGS. 2A-2C can be translated intorelative numerical values. The resulting values can then be stored in acoating information database 140.

For example, FIG. 3 depicts a number line 330 with indications 300, 310,320 of each respective example coating 200, 210, 220. In particular,FIG. 3 shows that the “X” indication 300 represents the first examplecoating 200, while the square indicator 310 represents the secondexample coating 210, and the circle indicator 320 represents the thirdexample coating 220. The illustrated number line 330 may represent theexamples coatings 200, 210, 220 relative relationships to each otherwith respect to their overall perceived texture. One will understandthat the number line 330 is merely exemplary and is provided for thesake of clarity and example. In practice the relationship betweenvarious texture characteristics of different coatings may comprise a farmore complex, multi-variable relationship that is much more difficult toconveniently depict and describe. Accordingly, the number line 330 isprovided as a simplified example to establish various innovative andnovel features in implementations of the present invention.

The human-perspective texture comparison charts 230, 240, 250 of FIGS.2A-2C comprise five different relative indications 260(a-e). In at leastone implementation, a relative value can be assigned to each indicatorfor each comparison between two respective example coatings, with one ofthe example coatings being considered the “target” from which the otherexample coating is to be compared. For example, the “a lot less thantarget” indicator 260 a may be assigned a relative value of −2, the “alittle less than target” indicator 260 b may be assigned a relativevalue of −1, the “relatively equal to target” indicator 260 c may beassigned a relative value of 0, the “a little more than target”indicator 260 d may be assigned a relative value of +1, and the “a lotmore than target” indicator 260 e may be assigned a relative value of+2. One will understand that the above provided integers of −2, −1, 0,+1, +2 are provided for the sake of example and clarity. Variousimplementations can utilize different schemes, including non-integer andnon-numerical schemes to quantify human-perception.

Returning to the human-perspective texture comparison in FIG. 2A withrespect to “overall perceived texture,” the user indicated that thefirst example coating 200 comprises “a little less” overall perceivedtexture than the second example coating 210. As such, a numerical valueof −1 can be assigned to the first example coating 200 with respect tothe second example coating 210.

In FIG. 2B with respect to “overall perceived texture,” the userindicated that the first example coating 200 comprises “a lot more”overall perceived texture than the third example coating 220. As such, anumerical value of +2 can be assigned to the first example coating 200with respect to the third example coating 220.

In FIG. 2C with respect to “overall perceived texture,” the userindicated that the third example coating 220 comprises “a lot less”overall perceived texture than the second example coating 210. As such,a numerical value of −2 can be assigned to the third example coating 220with respect to the second example coating 210.

An analysis of the above human-perspective texture comparison datareveals that the third example coating 220 comprises “a lot less”overall perceived texture than both the first example coating 200 andthe second example coating 210. This conclusion can be reached basedupon the assumption that the human-perspective texture comparison datain FIG. 2B, which indicates that the first example coating 200 comprises“a lot more” perceived texture than the third example coating 220, isthe equivalent to the third example coating 220 comprising “a lot less”perceived texture than the first example coating 200. A further, similaranalysis of the human-perspective texture comparison data reveals thesecond example coating 210 comprise a little more overall perceivedtexture than the first example coating 200 and a lot more overallperceived texture than the third example coating 220.

These relationships can be depicted by placing the “X” indicator 300 forthe first example coating 200 at “0” on the number line 330. In thisexample, the first example coating 200 is placed at the “0” as a form ofnormalizing the numerical relationships around the medianhuman-perspective texture comparison data point—in this case, the firstexample coating 200. The above data indicated that the second examplecoating 210 was +1 higher in texture than the first example coating 200.This relationship can be represented by placing the square indicator 210for the second example coating 210 on the “+1” on the number line 330.

The placement of the third example coating 220 on the number line 300may comprise accounting for two different human-perspective texturecomparison data points. For example, the human-perspective texturecomparison data indicates that the third example coating 220 comprises“a lot less” overall perceived texture than the second example coating210. Additionally, the human-perspective texture comparison dataindicates that the first example coating 200 comprises “a lot more”overall perceived texture than the third example coating 220. In otherwords, assigning a numerical value to the relationships would requirethat the third example coating 220 be assigned a numerical value of −2with respect to both the first example coating 200 and the secondexample coating 210.

Because the first example coating 200 and the second example coating 210have different overall perceived textures with respect to each other, inat least one implementation, the numerical value of −2 assigned to thethird example coating 220 can be treated as a minimum difference. Assuch, the third example coating 220 can be placed on the number line330, such that it is at least a numerical value of −2 lower than boththe first example coating 200 and the second example coating 210. Thisrelationship is depicted in FIG. 3 by placing the circle indicator 320for the third example coating 220 at the “−2”, while placing the “X”indicator 300 for the first example coating 200 at “0” on the numberline 330, and placing the square indicator 210 for the second examplecoating 210 on the “+1” on the number line 330.

While the number line 330 of FIG. 3 is limited to data from the firstexample coating 200, the second example coating 210, and the thirdexample coating 220, one will appreciate that in at least oneimplementation, the number line 330 can comprise information from acompany's entire coating portfolio or from a large number of randomcoatings. Creating a number line 330 that accounts for a large number ofcoatings may result in a standard basis by which any coating (whetheroriginally accounted for on the number line or not) can be rated. Statedmore generally, comparing a large number of different coatings, andtheir associated textures, can result in a standard comparison metric bywhich textures can be universally compared. The universal standard mayallow a user to enter a single coating into the coating texturecalculation software application 100 and receive an indication of howthe coating's texture compares with respect to the large number ofrandomly entered coating textures. Accordingly, in at least oneimplementation, the coating texture calculation software application 100can provide standardized indicators regarding the texture of aparticular coating, without requiring the user to enter specificcomparison coatings.

FIG. 4 depicts a coating analysis output data table 400 comprisingexample data received from a spectrophotometer 110. FIG. 4 depicts fourexemplary data variables (λ, δ, σ, and θ) for the first example coating200, the second example coating 210, and the third example coating 220,respectively. As used herein, the data variables, λ, δ, σ, and θ, aremerely exemplary. Various different spectrophotometers may provideunique proprietary output data. Similarly, in implementations where thecoating texture calculation software application 100 processes images(i.e., photographs) itself, it may also provide a unique data set ofoutput variables. Accordingly, the examples provided here with respectto variables λ, δ, σ, and θ are merely for the sake of clarity anddiscussion and should not be read to limit the present invention to anyparticular method, system, or apparatus.

In at least one implementation, the coating analysis output data table400 and the human-perspective texture comparison charts 230 can bestatistically analyzed with pattern matching algorithms, machinelearning techniques, or otherwise analyzed to identify correlations andpatterns between the various variables within the data table 400 and therelative texture characteristics obtained by human-perspective texturecomparisons. For example, it may be identified that there is an inverserelationship between the difference between λ and δ and the overallperceived texture of a coating. For example, with respect to the thirdexample coating 220, λ is 114 and 6 is 21, which results in a differenceof 93. In contrast, the differences between λ and δ for the firstexample coating 210 and the second example coating 200 are 36 and 7,respectively. As such, the third example coating 220 with the leastamount of overall perceived texture comprises the greatest differencebetween λ and δ, while the second example coating with the greatestamount of overall perceived texture comprises the least differencebetween λ and δ.

In at least one implementation, correlations and/or relationships can beidentified between the coating analysis output data table 400 and a widevariety of different random coatings. Additionally, the identifiedcorrelations and/or relationships can be used to derive formulasdescribing the identified correlations and/or relationships. As such,the coating texture calculation software application 100 can process anew, unique coating and interpolate various human-perspective texturecharacteristics.

For example, FIG. 5 depicts a graph 500 of the differences between λ andδ for the first, second, and third example coatings, along with theirrespective overall perceived texture. In particular, the graph 500depicts overall perceived texture 520 on the Y-axis and the differencebetween λ and δ 510 on the X-axis. Additionally, using conventionalcurve fitting algorithms or other complex statistical analysis, anequation can be developed that draws a line 530 between each of therespective data points 310, 300, 320.

In at least one implementation, the equation can then be used tointerpolate the overall perceived texture for other coatings, based uponthe λ and δ variables received from the respective target coating. Whilethe equation of FIG. 5 is depicted as being linear and only dependingupon the difference between λ and δ, in at least one implementation, therelationship between the received output variables and a particularperceived texture characteristic may be far more complex. As such, thegraph 500 and relationship depicted in FIG. 5 is provided only for thesake of example and clarity.

The resulting identified correlations and/or relationships can be usedin the methods and systems according to the present invention forassisting users in easily and quickly evaluating and/or matchingcoatings based upon coating characteristics. For example, FIG. 6 depictsan exemplary coating information user interface 600 that can begenerated by the display module 150 of FIG. 1. In the depictedimplementation, the coating texture information user interface 600 isdepicted as a webpage within a web browser; however, in alternateimplementations the user interface 600 may be depicted within astandalone application, in association with a purpose-made textureidentification device, or within any other system that provides adisplay device.

As disclosed above, at least one implementation of creating a userinterface 600 comprises first receiving from a coating-measurementinstrument one or more coating variables, wherein coating variablescomprise data variables received from a particular coating-measurementdevice. The coating-measurement instrument can comprise a spectrometerfor detecting color data, a camera-enabled spectrometer for detectingcolor and texture data, a camera for gathering color and texture data,or any other device capable of measuring color characteristics andproviding texture variables. The coating variables may describe variouscoating characteristics associated with a target coating. For example,the coating variables may comprise one or more variables that areassociated with one or more coating sparkle color characteristics,coating texture characteristics, coating color characteristics, coatingcolor travel characteristics, or any other available coatingcharacteristics provided by conventional coating-measurementinstruments. The received coating variables may be in the form ofvarious variables that have to be correlated to the desired coatingsparkle color characteristics, coating texture characteristics, coatingcolor characteristics, and/or coating color travel characteristics. Thereceived coating characteristics may comprise proprietary informationthat is specific to a particular measurement device. The proprietaryinformation can be mapped to desired texture attributes, such as thecharacteristics listed above. As such, an entire set of received coatingvariables may comprise subsets of variables that can be grouped ascoating sparkle color characteristics, coating texture characteristics,coating color characteristics, coating color travel characteristics, orany other available coating characteristics provided by conventionalcoating-measurement instruments.

Using the systems and methods described above, in variousimplementations, the received coating variables can be used to calculatesparkle color ratings. For example, techniques similar to thosedisclosed in PCT/US2015/057782, which is hereby incorporated byreference in its entirety, can be used for calculating a sparkle colorrating. In short, an image of a coating can be obtained from a camera, acamera-enabled spectrometer, or any other source. The distribution ofcolored sparkles may then be determined within a coating at a multitudeof angles. Because micas and xirallics change colors uniquely overvarious viewing angles and conditions, the appropriate pearl may beselected for a search or formulation algorithm, and a relative ratio asto the amount of each required to match the target coating may beestimated. Also, the sparkle color may be used to assist in selectionof, for example, appropriate aluminums and other special effect pigmentssuch as glass flake because the color of such materials does not shiftover various angles. Thus, various ratios can be determined—for example,ratios of aluminums to pearls in effect coatings or ratios of greenaluminum flakes to yellow aluminum flakes.

In various additional or alternative embodiments, a high pass filter maybe applied to the target image to determine the brightest spots amongstthe various pixels in the image. The resultant data/image may includeinformation on only the bright locations. The high pass filter mayconvolve a matrix of values with a high value center point and low valueedge points with the matrix of intensity information of the image. Thisisolates high intensity pixels. To further refine the sparkle points, anedge detection method of filtering may be applied in conjunction withthe intensity filtering.

In various additional or alternative embodiments individual sparklepoints may be labeled and counted, thus isolating/labeling them basedupon hue range. As such, the described method may result in a count oflabeled sparkle points, each meeting criteria based upon the individualhue criteria, which can then be formatted and output as desired.

Additional or alternative embodiments may include the use of a series ofhue-based band pass filters that identify individual hue regionsindependent of lightness and/or brightness. Regional labeling andanalysis of chroma and lightness (and/or brightness) of the regions maybe used to isolate individual sparkle points within each hue band. Sucha technique may determine sparkle points while estimating the percentageof the image that falls within each hue to enable a relatively quickanalysis of color change in sparkle color over multiple images tosupplement any further identification. In various embodiments, a bandstop filter may be used in place of or in combination with a band passfilter.

As used herein, the sparkle color ratings can indicate a similaritybetween the one or more coating sparkle color characteristics of atarget coating and respective coating sparkle color characteristics ofeach of the respective proposed coating matches. For example, the targetcoating may comprise a yellow-green aluminum flake color, while at leastone of the proposed coating matches may comprise a yellow-blue aluminumflake color.

In at least one additional or alternative implementation, the sparklecolor rating can be calculated by calculating a percentage match of thesparkle color and ratio information from a target coating and thesparkle color and ratio information from one or more proposed matchcoatings. For example, the target coating may comprise a ratio ofyellow-to-blue flakes of 1 yellow flake for every 2 blue flakes. Incontrast, a proposed matched coating may comprise a ratio of 1 yellowflake for every 4 blue flakes. A resulting sparkle color rating may be50% because the proposed match only comprises 50% of the target coatingssparkle color ratio.

Additionally, in additional or alternative embodiments, human perceptioncan be used to calculate a relative sparkle color rating. For example,using techniques described above, a large group of individuals canprovide their perceptions relating to comparative sparkle colors ofseveral different coatings. Correlation functions can then be calculatedbased upon statistical relationships between the above calculatedsparkle color ratios and the human perception data. The correlationfunction can then generate sparkle color ratings as described above.

Additionally, the received coating characteristics can be used tocalculate human-perceived effect texture ratings. For example, thecoating characteristics can be used to calculate human-perceived effecttexture ratings for multiple respective proposed coating matches. Asused herein, the effect textures ratings are also referred to herein asoverall perceived textures (as shown and described with respect to FIGS.2A-2C). The effect texture rating can indicate a similarity between theone or more coating characteristics of a target coating and respectivecoating textures characteristics of each of the respective proposedcoating matches. For example, the target coating may comprise arelatively coarse aluminum flake effect, while at least one of theproposed coating matches may comprise a relatively fine aluminum flakeeffect.

As such, in at least one implementation, the effect texture rating canindicate whether the one or more coating characteristics associated witheach respective proposed coating match is more coarse or more fine thanthe target coating. Additionally, in at least one implementation, theeffect texture rating can indicate whether the one or more coatingcharacteristics associated with each respective proposed coating matchcomprises more or less texture than the target coating. In at least oneimplementation, the indicated similarities are determined usingcorrelations based upon human-perceived texture differences, asdisclosed above.

The received coating variables can also be used to calculateconventional color coating ratings and/or human-perceived coating colorratings. For example, the coating color characteristics can becalculated using known color matching techniques that are thennormalized to a desired scale for display. Additionally oralternatively, the coating variables can be used to calculatehuman-perceived coating color ratings for multiple respective proposedcoating matches using methods similar to those described above withrespect to FIGS. 2A-2C. For example, a correlation can be identifiedbetween the visual perceptions of multiple users regarding colorsimilarity and the coating variables. As used herein, the coating colorratings can indicate a similarity between the one or more coating colorcharacteristics of a target coating and respective coating colorcharacteristics of each of the respective proposed coating matches. Forexample, the target coating may comprise a deep blue color, while atleast one of the proposed coating matches may comprise a midnight bluecolor. In at least one implementation, the indicated similarities aredetermined using correlations based upon human-perceived texturedifferences, as disclosed above.

Further, the received coating variables can also be used to calculatehuman-perceived color travel ratings. For example, the coating colortravel variables can be used to calculate color travel usingconventional methods or to calculate human-perceived color travelratings for multiple respective proposed coating matches using methodssimilar to those described above. As used herein, the coating colortravel ratings can indicate a similarity between the one or more coatingcolor travel characteristics of a target coating and respective coatingcolor travel characteristics of each of the respective proposed coatingmatches. For example, the target coating may comprise a color thattravels from a blue color to a green color over a specific angletransition, while at least one of the proposed coating matches maycomprise a color that travels from a blue color to a gold color over thesame angle transition. In at least one implementation, the indicatedsimilarities are determined using correlations based uponhuman-perceived texture differences, as disclosed above.

Once a desired rating or combination of ratings is calculated, acomputer processor that is in communication with a display device cansend instructions to generate a graphical representation ofcoating-related information on a user interface that depicts a visualordering of at least a portion of the proposed coating matches. Theoverall rankings may indicate a similarity between the target coatingand each of the proposed coating matches with respect to one or more ofthe various ratings.

In various implementations, the overall rankings may comprise a singlerating for each proposed match that indicates the overall texturesimilarity between each respective proposed match and the targetcoating. In contrast, in at least one implementation, the overallrankings comprise one or more of a sparkle color rating, an effecttexture rating, a coating color rating, or a color travel rating foreach respective proposed match coating. Further, in at least oneimplementation, the ratings may be with respect to an entire colorspace, such that the target coating is associated with one or moreratings that are not with respective to specific proposed matchcoatings.

Returning now to the user interface 600 of FIG. 6, the user interface600 depicts various implementations of ratings and other coatinginformation. One will understand, however, that the depicted userinterface is provided for clarity and is not meant to limitimplementations of the user interface 600 to particular visual elements,layouts, or other similar depicted features.

The user interface 600 comprises various elements, including, a proposedmatch element 610, an image of the target coating 615, and a matchinformation section 620. The proposed match element 610 may compriseinformation about a particular coating that was selected based upon itcomprising the closest overall match to the target coating 615. Forexample, the proposed match element 610 may comprise information about aproposed match coating that comprises the least average differentiationbetween the proposed match coating's texture and color characteristicsand the texture and color characteristics gathered from the targetcoating. Additional proposed match coatings may be ordered based using asimilar calculation, where they are order by least averagedifferentiation to greatest average differentiation.

The match information section 620 comprises various data columns 630,640, 650, 660, 670, 680, 690, 695 for providing information about eachrespective proposed match. For example, the depicted exemplary matchinformation section 620 comprises a proposed match coating image column630, a match rating column 640, a sparkle color column 650, an effecttexture column 660, a color travel column 670, a coating manufacturercolumn 680, a coating description column 690, and a paint system column695. In alternate implementations, the match information section 620 mayalso comprise a column for effect coarseness that indicates whether acoating is more or less coarse than the target coating.

The proposed match coating image column 630 can comprise images of eachrespective proposed match coating. In at least one implementation, theimages may comprise pictures of each of the proposed match coatingstaken under similar light conditions. A user may compare the imageswithin the proposed match coating image column 630 to the image of thetarget color 615. The ability to compare the images of the proposedcoatings 630 with the image of the target coating 615 provides a userwith the ability to visually distinguish between potential matches.

The match rating column 640 can comprise color coating ratings. Thecolor coating ratings may indicate a similarity between the coatingcolor characteristics of the target coating and respective coating colorcharacteristics of each of the proposed coating matches. The colorcoating ratings may be depicted in a variety of different forms. Forexample, in FIG. 6, the color coating ratings are depicted as anumerical value (with the lower numbers being a closer match to thetarget coating).

In at least one implementation, the depicted number may be derived usingthe human-perceived ratings described above. For example, the depictednumerical values may be derived using a correlation similar to thatdepicted and described with respect to FIGS. 3 and 5. Additionally, inat least one implementation, the resulting values from the human-derivedcorrelation function can be normalized to fit a particular scale (e.g.,1-100). Further, to increase the ease with which the number can beinterpreted, in at least one implementation, the numbers can also becolor-coded such that lower numbers are a particular color on a spectrum(e.g., green) while higher numbers are a different color on the spectrum(e.g., yellow or red).

The sparkle color column 650 can comprise sparkle color ratings. Thesparkle color ratings may indicate a similarity between the coatingsparkle color characteristics of the target coating and respectivecoating sparkle color characteristics of each of the proposed coatingmatches. The sparkle color ratings may be depicted in a variety ofdifferent forms. For example, in FIG. 6, the color coating ratings aredepicted as a numerical value. The numerical values and depictionsthereof may also utilize the human-perceived ratings described above. Inthe depicted implementation, the higher sparkle color ratings indicate acloser match. One will note that this association is opposite from theassociation within the matching ratings column 640. In at least oneimplementation, displaying contrasting rating systems may help preventinadvertent misreading by a user.

The effect texture column 660 can comprise effect ratings. The effecttexture ratings may indicate a similarity between the coating texturecharacteristics of the target coating and respective coating texturecharacteristics of each of the proposed coating matches. The effecttexture ratings may be depicted in a variety of different forms. Forexample, in FIG. 6, the color coating ratings are depicted as agraphical slider.

The graphical slider may comprise various colors and/or increments thatvisually depict the similarity (or dissimilarity) between the effecttexture of the target coating the effect textures of the variousrespective proposed matching coatings. In at least one implementation,the graphical slider values may be derived using the human-perceivedratings described above. For example, the relative values associatedwith the graphical slider may be derived using a correlation similar tothat depicted and described within respect to FIGS. 3 and 5.Additionally, in at least one implementation, the resulting values fromthe human-derived correlation function can be normalized to fit aparticular scale (e.g., −2 to +2).

Further, to increase the ease with which the number can be interpreted,in at least one implementation, the graphical slider can also becolor-coded such that values within an ideal range are a particularcolor on a spectrum (e.g., green) while values outside of the idealrange are a different color on the spectrum (e.g., yellow or red). In atleast one implementation, in addition to providing a user within anindication about the similarity between effect textures, the graphicalslider also allows a user to determine whether a proposed matchingcoating comprises more or less effect texture than the target coating.

The color travel column 670 can comprise color travel ratings. The colortravel ratings may indicate a similarity between the coating colortravel characteristics of the target coating and respective coatingcolor travel characteristics of each of the proposed coating matches.The color travel ratings may be depicted in a variety of differentforms. For example, in FIG. 6, the color coating ratings are depicted asa textual description of the match. In particular, the texturedescriptions comprise descriptions such as “OK,” “Face: Lighter,” “Face:Bluer,” “Flop: Yellower,” and “Flow: Redder, Yellower.” As such, a usermay be provided with an indication that the color travel is a match(i.e., “OK”) or a description of the differences in the color travel.

In at least one implementation, the textual description may be derivedusing the human-perceived ratings described above. For example, thedepicted textual description may be derived using a correlation similarto that depicted and described with respect to FIGS. 3 and 5. Forexample, the resulting correlation values may be associated withspecific color differences (e.g., yellower, redder, etc.). Whendescribing the color travel of various proposed match coating, adescription “OK” can be used to describe colors that fall within adesired threshold of the target coating. Color travel ratings associatedwith proposed match coatings that fall outside the threshold can then bedescribed with a color difference, as determined by the human-perceivedcorrelations.

The coating manufacturer column 680, coating description column 690, andpaint system column 695 each depict various textual information relatingto each respective proposed match coating. The depicted information canbe gathered from a coating information database 140 (depicted in FIG.1). The information may assist a user in both verifying a match and inordering or mixing a desired coating. For example, the user may besearching for a paint match for a particular brand of automobile.Verifying that the proposed match coating is associated with themanufacturer for the particular brand of automobile may server tofurther verify that a proposed match is correct.

Accordingly, FIG. 6 depicts an implementation of a user interface 600that depicts various columns 630, 640, 650, 660, 670, 680, 690, 695 forproviding information about each respective proposed match. The depictedcolumns comprise various different visual depictions of ratings,including a graphical slider, a numerical value, and a textualdescription. While the visual depictions of ratings are each associatedwith particular columns and data in FIG. 6, one will understand that theparticular visual depictions of ratings are only shown for the sake ofexample, and in alternate implementations, the respective visualdepictions of ratings may be associated with other columns and data. Assuch, any of rating within the columns 630, 640, 650, 660, 670, 680,690, 695 may be properly depicted using any combination of graphicalsliders, numerical values, and/or textual descriptions.

In at least one implementation, when generating a user interface 600 thesystem can receive from a user a preferred characteristic. The preferredcharacteristic may comprise effect texture ratings, sparkle colorratings, coating color ratings, color travel ratings, or any otherrating of interest. The overall rankings, or ordering, of the proposedcoating matches may then by sorted based upon the preferredcharacteristic received from the user.

For example, a user may be particularly interested in matching the colortravel of a target paint. Selecting the color travel rating as apreferred characteristic can cause the system to sort the proposed matchcoatings such that the color travel rating is prioritized. Prioritizinga particular characteristic may comprise simply sorting by the preferredcharacteristics—without regard to the similarity of any othercharacteristics. In contrast, in an alternate implementation,prioritizing a particular characteristic may cause the system to relyupon the preferred characteristic when breaking ties between proposedmatching colors. Further, in at least one implementation, prioritizing aparticular characteristic may cause the system to weigh the preferredcharacteristic with respect to the other characteristics such thatproposed match coatings with similar preferred characteristics aresorted higher in the ranking than they otherwise would have been.

Similarly, in at least one implementation, when generating a userinterface 600 the system can receive from a user one or morecharacteristic thresholds. The one or more characteristic thresholds maycomprise user-defined acceptable thresholds relating to effect textureratings, sparkle color ratings, coating color ratings, color travelratings, or any other rating of interest. The overall rankings, orordering, of the proposed coating matches may then be sorted based uponthe user-defined acceptable thresholds received from the user.

For example, a user may be particularly interested in matching theeffect texture of a target paint. To ensure a close match, the user canset a user-defined acceptable threshold for effect texture of +/−3. Inat least one implementation, the system will exclude all proposedcoating matches that fail to meet the user-defined acceptablethresholds.

Turning now to FIG. 7, FIG. 7 depicts another exemplary coating textureinformation user interface 700. In particular, FIG. 7 depicts a userinterface 700 for describing only the target coating without anyproposed match coatings. In the depicted implementation, the userinterface 700 comprises an image 710, or photograph, of the targetcoating. Additionally, the user interface 700 depicts a name 712associated with the target coating, a color value 714, and an overalltexture rating 720. The name 712 may be provided manually by a user, maycomprise a temporary sample name, or may be otherwise provided. Thecolor value 714 may comprise a color match that is derived from thetarget coating itself. In various implementations, however, the colorvalue 714 may not be depicted.

The overall texture value 720 may comprise an overall texture value thatdescribes in a single value the texture of the target coating. Theoverall texture value 720 may be calculated directing fromhuman-perspective texture comparison charts 250 (shown in FIG. 2) thatdirectly request an “overall perceived texture” indication. In at leastone implementation, however, the overall texture value 720 can becalculated based upon a subset of other texture ratings, 740, 750, 760.For example, the overall texture rating 714 can be calculated byaveraging at least the sparkle color rating 740, the effect texturerating 750, and the color travel rating 760.

In at least one implementation, the user interface 700 can also comprisea data portion 770 that allows a user to view conventional texture data.For example, the data portion 770 may be visually expandable to displayall data and variables received from a coating-measurement instrument.In at least one implementation, however, the data portion 770 isinitially hidden from view, such that the interface 700 depicts a simpleand clean organization of information. Additionally, in at least oneimplementation, the texture ratings 730, 740, 750, 760, other than theoverall rating 720, are also not depicted initially.

As depicted, the user interface 700 can display various texture ratings720, 740, 750, 760 for a target color 710 without respect to particularproposed match coating ratings. In at least one implementation, thevarious depicted ratings 720, 730, 740, 750, 760 are calculated withrespect to a large number of other coating configurations. For example,the ratings 720, 730, 740, 750, 760 may be calculated with respect to aevery other coating stored within the coating information database 140.In contrast, in at least one implementation, the ratings 720, 730, 740,750, 760 can be calculated with respect to a specific color space,defined either automatically or by a user. For example, a user may wishto know the each of the ratings 720, 730, 740, 750, 760 for the targetcolor 710 with respect to the coatings provided by a particularmanufacturer. As such, each of the ratings 720, 730, 740, 750, 760 canbe calculated as compared to every coating provided by that particularmanufacturer.

Accordingly, FIGS. 1-7 and the corresponding text depict or otherwisedescribe various implementations of the present invention that areadapted to analyze a paint sample and generate values that describevarious attributes of a target coating. In particular, theimplementations of a user interface described above can display simpletexture ratings that describe various unique aspects of texturecharacteristics. Additionally, in at least one implementation, a userinterface can display ratings that are based upon human-perceptions ofvarious textures and coatings.

For example, FIG. 8 illustrates that a method for matching a paintsample to various proposed paint coatings can include an act 800 ofreceiving coating variables. Act 800 can comprise receiving one or morecoating variables of a target coating from a coating-measurementinstrument. For example, as depicted and described with respect to FIG.1, a coating texture calculation software application 100 (e.g.,executed on computer system 160) can receive from a camera-enabledspectrophotometer 110 various coating variables.

Additionally, FIG. 8 shows that the method can include, in any order,one or more of act 810, act 820, act 830, and/or act 840 for displayingeffect texture ratings, sparkle color ratings, coating color ratings,and/or coating travel ratings, respectively. Acts 810-840 can comprisedisplaying respective ratings 810, 820, 830, 840 for multiple respectiveproposed coating matches on a digital display device. The respectiveratings can indicate a similarity between the one or more coatingcharacteristics of the target coating and respective coatingcharacteristics of each of the respective proposed coating matches. Forexample, as depicted and described in FIGS. 6 and 7 and the accompanyingdescriptions, a user interface 600, 700 can display any combination ofan overall texture rating 720, a match rating 640, 730, a sparkle colorrating 650, 740, an effect texture rating 660, 740, a color travelrating 670, 760, or any other coating rating.

FIG. 8 also shows that the method can include an act 850 of orderingproposed coating matches. Act 850 can comprise ordering at least aportion of the proposed coating matches. The ordering indicates astrength in similarity between the target coating and each of the atleast a portion of the proposed coating matches with respect to thecoating texture ratings. For example, as depicted and described withrespect to FIG. 6, a set of proposed coating matches 620 can be sortedbased upon the similarity of each respective proposed match coating withrespect to the various coating ratings 640, 650, 660, 670.

Accordingly, implementations of a texture matching computer system canprovide unique and novel methods and systems for processing anddisplaying coating-specific texture information. Additionally,implementations of a texture information user interface can display asingle overall texture rating or a set of attribute-specific textureratings. Further, in at least one implementation, the various textureratings can be based upon human-perceived correlations.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above,or the order of the acts described above. Rather, the described featuresand acts are disclosed as example forms of implementing the claims.

The present invention may comprise or utilize a special-purpose orgeneral-purpose computer system that includes computer hardware, suchas, for example, one or more processors and system memory, as discussedin greater detail below. Embodiments within the scope of the presentinvention also include physical and other computer-readable media forcarrying or storing computer-executable instructions and/or datastructures. Such computer-readable media can be any available media thatcan be accessed by a general-purpose or special-purpose computer system.Computer-readable media that store computer-executable instructionsand/or data structures are computer storage media. Computer-readablemedia that carry computer-executable instructions and/or data structuresare transmission media. Thus, by way of example, and not limitation,embodiments of the invention can comprise at least two distinctlydifferent kinds of computer-readable media: computer storage media andtransmission media.

Computer storage media are physical storage media that storecomputer-executable instructions and/or data structures. Physicalstorage media include computer hardware, such as RAM, ROM, EEPROM, solidstate drives (“SSDs”), flash memory, phase-change memory (“PCM”),optical disk storage, magnetic disk storage or other magnetic storagedevices, or any other hardware storage device(s) which can be used tostore program code in the form of computer-executable instructions ordata structures, which can be accessed and executed by a general-purposeor special-purpose computer system to implement the disclosedfunctionality of the invention.

Transmission media can include a network and/or data links which can beused to carry program code in the form of computer-executableinstructions or data structures, and which can be accessed by ageneral-purpose or special-purpose computer system. A “network” isdefined as one or more data links that enable the transport ofelectronic data between computer systems and/or modules and/or otherelectronic devices. When information is transferred or provided over anetwork or another communications connection (either hardwired,wireless, or a combination of hardwired or wireless) to a computersystem, the computer system may view the connection as transmissionmedia. Combinations of the above should also be included within thescope of computer-readable media.

Further, upon reaching various computer system components, program codein the form of computer-executable instructions or data structures canbe transferred automatically from transmission media to computer storagemedia (or vice versa). For example, computer-executable instructions ordata structures received over a network or data link can be buffered inRAM within a network interface module (e.g., a “NIC”), and theneventually transferred to computer system RAM and/or to less volatilecomputer storage media at a computer system. Thus, it should beunderstood that computer storage media can be included in computersystem components that also (or even primarily) utilize transmissionmedia.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at one or more processors, cause ageneral-purpose computer system, special-purpose computer system, orspecial-purpose processing device to perform a certain function or groupof functions. Computer-executable instructions may be, for example,binaries, intermediate format instructions such as assembly language, oreven source code.

Those skilled in the art will appreciate that the invention may bepracticed in network computing environments with many types of computersystem configurations, including, personal computers, desktop computers,laptop computers, message processors, hand-held devices, multi-processorsystems, microprocessor-based or programmable consumer electronics,network PCs, minicomputers, mainframe computers, mobile telephones,PDAs, tablets, pagers, routers, switches, and the like. The inventionmay also be practiced in distributed system environments where local andremote computer systems, which are linked (either by hardwired datalinks, wireless data links, or by a combination of hardwired andwireless data links) through a network, both perform tasks. As such, ina distributed system environment, a computer system may include aplurality of constituent computer systems. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Those skilled in the art will also appreciate that the invention may bepracticed in a cloud-computing environment. Cloud computing environmentsmay be distributed, although this is not required. When distributed,cloud computing environments may be distributed internationally withinan organization and/or have components possessed across multipleorganizations. In this description and the following claims, “cloudcomputing” is defined as a model for enabling on-demand network accessto a shared pool of configurable computing resources (e.g., networks,servers, storage, applications, and services). The definition of “cloudcomputing” is not limited to any of the other numerous advantages thatcan be obtained from such a model when properly deployed.

A cloud-computing model can be composed of various characteristics, suchas on-demand self-service, broad network access, resource pooling, rapidelasticity, measured service, and so forth. A cloud-computing model mayalso come in the form of various service models such as, for example,Software as a Service (“SaaS”), Platform as a Service (“PaaS”), andInfrastructure as a Service (“IaaS”). The cloud-computing model may alsobe deployed using different deployment models such as private cloud,community cloud, public cloud, hybrid cloud, and so forth.

Some embodiments, such as a cloud-computing environment, may comprise asystem that includes one or more hosts that are each capable of runningone or more virtual machines. During operation, virtual machines emulatean operational computing system, supporting an operating system andperhaps one or more other applications as well. In some embodiments,each host includes a hypervisor that emulates virtual resources for thevirtual machines using physical resources that are abstracted from viewof the virtual machines. The hypervisor also provides proper isolationbetween the virtual machines. Thus, from the perspective of any givenvirtual machine, the hypervisor provides the illusion that the virtualmachine is interfacing with a physical resource, even though the virtualmachine only interfaces with the appearance (e.g., a virtual resource)of a physical resource. Examples of physical resources includingprocessing capacity, memory, disk space, network bandwidth, mediadrives, and so forth.

The present invention therefore relates in particular, without beinglimited thereto, to the following aspects:

-   -   1. A computer implemented method comprising:        -   receiving, using at least one processor, one or more coating            characteristics of a target coating selected from sparkle            color characteristics, coating texture characteristics,            coating color characteristics and coating color travel            characteristics and combinations thereof from a            coating-measurement instrument;        -   accessing, using the processor, a database comprising            corresponding coating characteristics for a plurality of            reference coatings and associated comparative human ratings            of the visual appearance of different reference coatings;        -   identifying, using the processor, a plurality of prospective            matching coatings from the plurality of reference coatings;        -   determining, using the processor, based on the received one            or more coating characteristics of the target coating and            the data stored in the database, ratings indicating            similarity between the target coating and each of the            identified prospective matching reference coatings with            respect to one or more of the coating characteristics;        -   displaying, using the processor, the determined ratings for            at least a portion of the plurality of prospective matching            reference coatings to a user; and        -   ordering, using the processor, at least a portion of the            prospective matching reference coatings based on the            determined ratings with respect to one or more of the            coating characteristics.    -   2. The computer implemented method according to aspect 1,        wherein:        -   The step of receiving, using the processor, one or more            coating characteristics of the target coating from the            coating-measurement instrument comprises receiving, using            the processor, one or more coating texture characteristics            of the target coating;        -   the step of determining ratings indicating similarity            between the target coating and each of the identified            prospective matching reference coatings with respect to one            or more of the coating characteristics comprises            determining, using the processor, based on the received one            or more coating texture characteristics of the target            coating and the data stored in the database, ratings            indicating similarity between the target coating and each of            the identified prospective matching reference coatings with            respect to one or more of the coating texture            characteristics; and        -   wherein the ordering is based on the determined ratings with            respect to one or more of the coating texture            characteristics.    -   3. The computer implemented method according to aspect 2,        wherein:        -   the step of receiving, using the processor, one or more            coating characteristics of the target coating from the            coating-measurement instrument further comprises receiving,            using the processor, one or more sparkle color            characteristics of the target coating;        -   the step of determining ratings indicating similarity            between the target coating and each of the identified            prospective matching reference coatings with respect to one            or more of the coating characteristics further comprises            determining, using the processor, based on the received one            or more sparkle color characteristics of the target coating            and the data stored in the database, ratings indicating            similarity between the target coating and each of the            identified prospective matching reference coatings with            respect to one or more of the sparkle color characteristics,            and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the sparkle color            characteristics.    -   4. The computer implemented method according to aspect 2 or 3,        wherein:        -   the step of receiving, using the processor, one or more            coating characteristics of the target coating from the            coating-measurement instrument further comprises receiving,            using the processor, one or more coating color            characteristics of the target coating;        -   the step of determining ratings indicating similarity            between the target coating and each of the identified            prospective matching reference coatings with respect to one            or more of the coating characteristics comprises            determining, using the processor, based on the received one            or more coating color characteristics of the target coating            and the data stored in the database, ratings indicating            similarity between the target coating and each of the            identified prospective matching reference coatings with            respect to one or more of the coating color characteristics,            and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the coating color            characteristics.    -   5. The computer implemented method according to any one of the        preceding aspects, further comprising:        -   receiving a preferred characteristic from a user input,            wherein the preferred characteristic is a coating texture            characteristic, a coating sparkle color characteristic, or a            coating color characteristic; and        -   wherein the ordering is determined by prioritizing the            preferred characteristic.    -   6. The computer implemented method according to any one of the        preceding aspects, further comprising:        -   receiving from a user one or more characteristic thresholds,            wherein the one or more characteristic thresholds define            user-defined acceptable thresholds relating to one or more            coating texture characteristic(s), one or more coating            sparkle color characteristic(s), and/or one or more coating            color characteristic(s); and        -   wherein the ordering accounts for the one or more            characteristic thresholds.    -   7. The computer implemented method according to any one of the        preceding aspects 2-6, wherein:        -   the step of receiving, using the processor, one or more            coating characteristics of the target coating from the            coating-measurement instrument further comprises receiving,            using the processor, one or more coating color travel            characteristics of the target coating;        -   the step of determining ratings indicating similarity            between the target coating and each of the identified            prospective matching reference coatings with respect to one            or more of the coating characteristics comprises            determining, using the processor, based on the received one            or more coating color travel characteristics of the target            coating and the data stored in the database, ratings            indicating similarity between the target coating and each of            the identified prospective matching reference coatings with            respect to one or more of the coating color travel            characteristics, and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the coating color travel            characteristics.    -   8. The computer implemented method according to aspect 7        comprising displaying, using the processor, the determined        ratings indicating similarity between the target coating and        each of the identified prospective matching reference coatings        with respect to one or more of the coating color travel        characteristics for at least a portion of the plurality of        prospective matching reference coatings to a user, wherein the        displaying comprises displaying a visual indication indicating a        difference in face or flop color.    -   9. The computer implemented method according to any one of the        preceding aspects 2-8 comprising displaying, using the        processor, the determined ratings indicating similarity between        the target coating and each of the identified prospective        matching reference coatings with respect to one or more of the        coating texture characteristics for at least a portion of the        plurality of prospective matching reference coatings to a user,        wherein the displaying comprises (a) displaying a visual        indication indicating whether the one or more coating texture        characteristics associated with each of the prospective matching        reference coatings is more coarse or more fine compared to the        target coating, and/or (b) displaying a visual indication        indicating whether the one or more coating texture        characteristics associated with each of the prospective matching        reference coatings comprise more or less texture compared to the        target coating.    -   10. The computer implemented method according to any one of the        preceding aspects wherein the coating-measurement instrument is        a camera-equipped spectrometer.    -   11. A system comprising:        -   a user interface comprising a display;        -   a database comprising one or more coating characteristics            for a plurality of reference coatings and associated            comparative human ratings of the visual appearance of            different reference coatings;        -   a coating-measurement instrument;        -   at least one processor in communication with the user            interface, the database and the coating-measurement            instrument, wherein the at least one processor is configured            to:        -   receive from the coating-measurement instrument one or more            coating characteristics of a target coating selected from            sparkle color characteristics, coating texture            characteristics, coating color characteristics, coating            color travel characteristics and combinations thereof;        -   identify a plurality of prospective matching coatings from            the plurality of reference coatings in the database;        -   determine, based on the received one or more coating            characteristics of the target coating and the data stored in            the database, ratings indicating similarity between the            target coating and each of the identified prospective            matching reference coatings with respect to one or more of            the coating characteristics;        -   display the determined ratings for at least a portion of the            plurality of prospective matching reference coatings on the            display to a user; and        -   order at least a portion of the prospective matching            reference coatings based on the determined ratings with            respect to one or more of the coating characteristics.    -   12. The system according to aspect 11, wherein the processor is        configured to:        -   receive from the coating-measurement instrument one or more            coating texture characteristics of the target coating;        -   determine, based on the received one or more coating texture            characteristics of the target coating and the data stored in            the database, ratings indicating similarity between the            target coating and each of the identified prospective            matching reference coatings with respect to one or more of            the coating texture characteristics; and        -   wherein the ordering is based on the determined ratings with            respect to one or more of the coating texture            characteristics.    -   13. The system according to aspect 12, wherein the processor is        further configured to:        -   receive from the coating-measurement instrument one or more            sparkle color characteristics of the target coating;        -   determine, based on the received one or more sparkle color            characteristics of the target coating and the data stored in            the database, ratings indicating similarity between the            target coating and each of the identified prospective            matching reference coatings with respect to one or more of            the sparkle color characteristics; and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the sparkle color            characteristics.    -   14. The system according to aspect 13, wherein determining the        ratings indicating similarity between the target coating and        each of the identified prospective matching reference coatings        with respect to one or more of the sparkle color characteristics        comprises calculating a coating sparkle color ratio of first        colored sparkles to second colored sparkles.    -   15. The system according to any one of aspect 12 to aspect 14,        wherein the processor is further configured to:        -   receive from the coating-measurement instrument one or more            coating color characteristics of the target coating;        -   determine, based on the received one or more coating color            characteristics of the target coating and the data stored in            the database, ratings indicating similarity between the            target coating and each of the identified prospective            matching reference coatings with respect to one or more of            the coating color characteristics; and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the coating color            characteristics.    -   16. The system according to any one of the preceding aspects        11-15, wherein the processor is further configured to:        -   receive via the user interface a preferred characteristic            from a user, wherein the preferred characteristic is a            coating texture characteristic, a coating sparkle color            characteristic, or a coating color characteristic; and        -   wherein the ordering is determined by prioritizing the            preferred characteristic.    -   17. The system according to any one of the preceding aspects        11-16, wherein the processor is further configured to:        -   receive via the user interface from a user one or more            characteristic thresholds, wherein the one or more            characteristic thresholds define user-defined acceptable            thresholds relating to one or more coating texture            characteristic(s), one or more coating sparkle color            characteristic(s), and/or one or more coating color            characteristic(s); and        -   wherein the ordering accounts for the one or more            characteristic thresholds.    -   18. The system according to any one of the preceding aspects        12-17, wherein the processor is further configured to:        -   receive from the coating-measurement instrument one or more            coating color travel characteristics of the target coating;        -   determine, based on the received one or more coating color            travel characteristics of the target coating and the data            stored in the database, ratings indicating similarity            between the target coating and each of the identified            prospective matching reference coatings with respect to one            or more of the coating color travel characteristics; and        -   wherein the ordering is also based on the determined ratings            with respect to one or more of the coating color travel            characteristics.    -   19. The system according to any one of the preceding aspects        12-18 wherein the processor is further configured to display to        the user on the display (a) a visual indication indicating        whether the one or more coating texture characteristics        associated with each of the prospective matching reference        coatings is more coarse or more fine compared to the target        coating, and/or (b) a visual indication indicating whether the        one or more coating texture characteristics associated with each        of the prospective matching reference coatings comprise more or        less texture compared to the target coating.    -   20. The system according to any one of the preceding aspects        11-19, wherein the coating-measurement instrument is a        camera-equipped spectrometer.    -   21. A non-transitory computer readable medium including software        for causing a processor to:        -   receive from a coating-measurement instrument one or more            coating characteristics of a target coating selected from            sparkle color characteristics, coating texture            characteristics, coating color characteristics, coating            color travel characteristics and combinations thereof;        -   access a database comprising corresponding coating            characteristics for a plurality of reference coatings and            associated comparative human ratings of the visual            appearance of different reference coatings;        -   identify a plurality of prospective matching coatings from            the plurality of reference coatings in the database;        -   determine, based on the received one or more coating            characteristics of the target coating and the data stored in            the database, ratings indicating similarity between the            target coating and each of the identified prospective            matching reference coatings with respect to one or more of            the coating characteristics;        -   display the determined ratings for at least a portion of the            plurality of prospective matching reference coatings on the            display to a user; and        -   order at least a portion of the prospective matching            reference coatings based on the determined ratings with            respect to one or more of the coating characteristics.

The present invention may be embodied in other specific forms withoutdeparting from its spirit or essential characteristics. The describedembodiments are to be considered in all respects only as illustrativeand not restrictive. The scope of the invention is, therefore, indicatedby the appended claims rather than by the foregoing description. Allchanges which come within the meaning and range of equivalency of theclaims are to be embraced within their scope.

We claim:
 1. A computer system for analyzing a coating sample andgenerating values that describe various attributes of a proposedmatching color, comprising: one or more processors; and one or morecomputer-readable media having stored thereon executable instructionsthat when executed by the one or more processors configure the computersystem to perform at least the following: receive from acoating-measurement instrument one or more coating variables of a targetcoating; calculate sparkle ratings for each of multiple proposed coatingmatches, wherein: the sparkle ratings indicate differences in sparklebetween a target coating sparkle and sparkle associated with each of themultiple proposed coating matches, and each sparkle ratings is derivedfrom a statistical mapping of the one or more coating variables tohuman-perceived rankings of relative sparkle differences between eachcoating in a set of coatings with respect to other coatings within theset of coatings, wherein the set of coatings does not include the targetcoating; and send instructions to generate a user interface that depictsoverall rankings of at least a portion of the multiple proposed coatingmatches, wherein the overall rankings indicate a similarity between thetarget coating and each of the at least a portion of the multipleproposed coating matches with respect to the sparkle ratings.
 2. Thecomputer system of claim 1, further comprising computer-executableinstructions that are executable by the one or more processors toconfigure the computer system to: receive from the coating-measurementinstrument one or more coating texture variables of the target coating;calculate effect texture ratings for multiple respective proposedcoating matches, wherein the effect texture ratings indicate asimilarity between the one or more coating texture variables of thetarget coating and respective coating textures variables of each of themultiple proposed coating matches; and wherein the overall rankings alsoindicate a similarity between the target coating and each of the atleast a portion of the multiple proposed coating matches with respect tothe effect texture ratings.
 3. The computer system of claim 2, furthercomprising computer-executable instructions that are executable by theone or more processors to configure the computer system to: receive fromthe coating-measurement instrument one or more coating color variablesof the target coating; calculate coating color ratings for the multiplerespective proposed coating matches, wherein the coating color ratingsindicate a similarity between one or more coating color characteristicsof the target coating, based upon the one or more coating colorvariables, and respective coating color characteristics of each of therespective proposed coating matches; and wherein the overall rankingsalso indicate a similarity between the target coating and each of the atleast a portion of the multiple proposed coating matches with respect tothe coating color ratings.
 4. The computer system of claim 3, furthercomprising computer-executable instructions that are executable by theone or more processors to configure the computer system to: receive froma user a preferred characteristic, wherein the preferred characteristicis coating texture, coating sparkle, or coating color; and wherein theoverall rankings are determined by prioritizing the preferredcharacteristic.
 5. The computer system of claim 3, further comprisingcomputer-executable instructions that are executable by the one or moreprocessors to configure the computer system to: receive from a user oneor more characteristic thresholds, wherein the one or morecharacteristic thresholds define user-defined acceptable thresholdsrelating to coating texture, coating sparkle, or coating color; andwherein overall rankings account for the one or more characteristicthresholds.
 6. The computer system of claim 2, wherein calculating theeffect texture ratings for the multiple respective proposed coatingmatches comprises determining whether one or more coating texturecharacteristics is more coarse or more fine than the target coating. 7.The computer system of claim 2, wherein calculating the effect textureratings for the multiple respective proposed coating matches comprisesdetermining whether one or more coating texture characteristicsassociated with each respective proposed coating match indicates more orless texture than the target coating.
 8. The computer system of claim 1,further comprising computer-executable instructions that are executableby the one or more processors to configure the computer system to:receive from the coating-measurement instrument one or more coatingtravel variables of the target coating; calculate coating color travelratings for each of the multiple proposed coating matches, wherein thecoating color travel ratings indicate a similarity between one or morecoating color travel characteristics of the target coating, based uponthe one or more coating travel variables, and respective coating colortravel characteristics of each of the multiple proposed coating matches;and wherein the overall rankings also indicate a similarity between thetarget coating and each of the at least a portion of the multipleproposed coating matches with respect to the coating color travelratings.
 9. The computer system of claim 1, wherein calculating thesparkle ratings for the multiple proposed coating matches comprisesidentifying a target coating sparkle ratio of first colored texturesparkles to second colored texture sparkles within the target coating.10. The computer system of claim 1, wherein the coating-measurementinstrument gathers image data from the target coating that is used tocalculate the sparkle ratings.
 11. A computerized method for executionon a computer system, comprising one or more processors, system memory,and one or more computer-readable media storing computer-executableinstructions, the computerized method for matching a paint sample tovarious proposed paint coatings, comprising the acts of: receiving oneor more coating variables of a target coating from a coating-measurementinstrument; displaying effect texture ratings for each of multipleproposed coating matches on a digital display device, wherein: theeffect texture ratings indicate differences in effect textures between atarget coating effect texture and effect textures associated with eachof the multiple proposed coating matches, and each effect texture ratingis derived from a statistical mapping of the one or more coatingvariables to human-perceived rankings of relative effect texturedifferences between each coating in a set of coatings with respect toother coatings within the set of coatings, wherein the set of coatingsdoes not include the target coating; and ordering at least a portion ofthe multiple proposed coating matches, wherein an ordering indicates astrength in similarity between the target coating and each of the atleast a portion of the multiple proposed coating matches with respect tothe effect texture ratings.
 12. The method as recited in claim 11,further comprising: receiving from the coating-measurement instrumentone or more sparkle variables of the target coating; displaying sparkleratings for the multiple proposed coating matches, wherein the sparkleratings indicate a similarity between the one or more sparkle variablesof the target coating and respective sparkle variables of each of therespective proposed coating matches; and wherein the ordering alsoindicates a strength in similarity between the target coating and eachof the at least a portion of the multiple proposed coating matches withrespect to the sparkle ratings.
 13. The method as recited in claim 12,further comprising: receiving from the coating-measurement instrumentone or more coating color variables of the target coating; displayingcoating color ratings for the multiple proposed coating matches, whereinthe coating color ratings indicate a similarity between one or morecoating color characteristics of the target coating, based upon the oneor more coating color variables, and respective coating colorcharacteristics of each of the respective proposed coating matches; andwherein the ordering also indicates a strength in similarity between thetarget coating and each of the at least a portion of the proposedcoating matches with respect to the coating color ratings.
 14. Themethod as recited in claim 13, further comprising: receiving a preferredcharacteristic from a user input, wherein the preferred characteristicis coating texture, coating sparkle, or coating color; and wherein theordering is determined by prioritizing the preferred characteristic. 15.The method as recited in claim 13, further comprising: receiving from auser one or more characteristic thresholds, wherein the one or morecharacteristic thresholds define user-defined acceptable thresholdsrelating to coating texture, coating sparkle, or coating color; andwherein the ordering accounts for the one or more characteristicthresholds.
 16. The method as recited in claim 11, further comprising:receiving from the coating-measurement instrument one or more coatingcolor travel variables of the target coating; displaying, on the digitaldisplay device, coating color travel ratings for the multiple proposedcoating matches, wherein the coating color travel ratings indicate asimilarity between one or more coating color travel characteristics ofthe target coating, based upon the one or more coating color travelvariables, and respective coating color travel characteristics of eachof the respective proposed coating matches; and wherein the orderingalso indicates strength in similarity between the target coating andeach of the at least a portion of the multiple proposed coating matcheswith respect to the coating color travel ratings.
 17. The method asrecited in claim 16, wherein displaying the coating color travel ratingsfor the multiple proposed coating matches comprises displaying a visualindication indicating a difference in face or flop color.
 18. The methodas recited in claim 11, wherein displaying the effect texture ratingsfor the multiple proposed coating matches comprises displaying a visualindication indicating whether one or more coating texturecharacteristics associated with each respective proposed coating matchis more coarse or more fine than the target coating.
 19. The method asrecited in claim 11, wherein displaying the effect texture ratings forthe multiple proposed coating matches comprises displaying an effecttexture visual indication indicating whether one or more coating texturecharacteristics associated with each respective proposed coating matchcomprises more or less texture than the target coating.
 20. A computerprogram product comprising one or more computer storage media havingstored thereon computer-executable instructions that, when executed at aprocessor, cause a computer system to perform a method for analyzing apaint sample and generating values that describe various attributes of aproposed matching color, the method comprising: receiving from acoating-measurement instrument one or more coating texture variables andone or more coating sparkle variables of a target coating; calculatingeffect texture ratings for each of multiple proposed coating matches ona digital display device, wherein: the effect texture ratings indicatedifferences in effect textures between a target coating effect textureand effect textures associated with each of the multiple proposedcoating matches, and each effect texture rating is derived from astatistical mapping of the one or more coating texture variables tohuman-perceived rankings of relative effect texture differences betweeneach coating in a set of coatings with respect to other coatings withinthe set of coatings, wherein the set of coatings does not include thetarget coating; calculating sparkle ratings for each of the multipleproposed coating matches, wherein: the sparkle ratings indicatedifferences in sparkle between a target coating sparkle and sparkleassociated with each of the multiple proposed coating matches, and eachsparkle rating is derived from a statistical mapping of the one or morecoating texture variables to human-perceived rankings of relativesparkle differences between each coating in a set of coatings withrespect to other coatings within the set of coatings, wherein the set ofcoatings does not include the target coating; and generating a userinterface that depicts overall rankings of at least a portion of themultiple proposed coating matches, wherein the overall rankings indicatea similarity between the target coating and each of the at least aportion of the multiple proposed coating matches with respect to theeffect texture ratings and the sparkle ratings.